Fig 1 - uploaded by Azlan Awang
Content may be subject to copyright.
Illustration of data aggregation mechanism in multihop wireless sensor networks a Data gathering at aggregator nodes and relaying toward a sink node. b The components of data aggregation
Source publication
In wireless sensor networks (WSNs), due to dense deployment, sensory data gathered by sensor nodes in close proximity tend to exhibit high correlation and therefore redundant. Transmitting such redundant data is not practical in the energy-constrained WSNs. Data aggregation offers a key solution to reduce such redundancy by allowing intermediate no...
Contexts in source publication
Context 1
... data aggregation, raw data from multiple sensor nodes are combined into an aggregated packet at an aggregator node before relaying it to a sink node, as illustrated in Fig. 1a. The data aggregation mechanism consists of two main tasks: data processing at intermediate nodes followed by data routing as shown in Fig. 1b. Data processing consists of the task to combine raw data using an aggregation function. 1 This requires the availability of data from multiple nodes at an aggregator point, at a particular ...
Context 2
... data aggregation, raw data from multiple sensor nodes are combined into an aggregated packet at an aggregator node before relaying it to a sink node, as illustrated in Fig. 1a. The data aggregation mechanism consists of two main tasks: data processing at intermediate nodes followed by data routing as shown in Fig. 1b. Data processing consists of the task to combine raw data using an aggregation function. 1 This requires the availability of data from multiple nodes at an aggregator point, at a particular instance of time. The availability of data from different source nodes at a single aggregator node requires convergence of data from different ...
Context 3
... transmissions include RTS, CTS, DATA and ACK packets and number of sources are the total number of nodes that generate data at any particular instance of time. Figure 10a gives the total number of RTS, CTS, DATA and ACK packets that are transmitted versus number of nodes. The number of packets transmitted depends on aggregation thus the curves for class-and alpha-based mechanisms without aggregation are almost the same. ...
Context 4
... an increase in the number of nodes, more data packets with the same Agg_ID are available for aggregation and we can observe that alpha-enCRT-Agg performs better than the class- based and alpha-based mechanisms. The number of packet receptions is directly related to the number of packet transmissions, hence the same kind of behavior is observed in Fig. 10b that gives the total number of packet receptions versus number of nodes. The DAA+RW exhibits about similar performance (slightly lower transmissions/receptions) with alpha-enCRT-Agg mechanism. Figure 11 depicts the normalized number of transmissions versus number of nodes. It can be observed that the curves for all the mechanisms (both ...
Context 5
... DAA+RW exhibits about similar performance (slightly lower transmissions/receptions) with alpha-enCRT-Agg mechanism. Figure 11 depicts the normalized number of transmissions versus number of nodes. It can be observed that the curves for all the mechanisms (both with aggregation and with- out aggregation) increases gradually with an increase in the number of nodes due to the increase in the total number of transmissions (see Fig. 10a). ...
Context 6
... with alpha-enCRT-Agg mechanism. Figure 11 depicts the normalized number of transmissions versus number of nodes. It can be observed that the curves for all the mechanisms (both with aggregation and with- out aggregation) increases gradually with an increase in the number of nodes due to the increase in the total number of transmissions (see Fig. 10a). In the case of no aggregation (class-enCRT-NoAgg and alpha-enCRT-NoAgg), both mechanisms have almost the same number of normalized transmissions since without aggregation both class-based and alpha- based mechanisms have similar functioning. Comparing among the aggregation based mech- anisms, i.e., class-uniCRT-Agg, class-enCRT-Agg ...
Context 7
... DAA+RW exhibits slightly lower number of normalised transmissions than alpha-enCRT-Agg. Figure 12a shows the packet loss probability versus number of nodes. Packet loss proba- bility gives a reliability indicator of any protocol and a low packet loss probability implies a more reliable protocol. ...
Context 8
... slightly lower number of normalised transmissions than alpha-enCRT-Agg. Figure 12a shows the packet loss probability versus number of nodes. Packet loss proba- bility gives a reliability indicator of any protocol and a low packet loss probability implies a more reliable protocol. Packet loss in WSNs generally occurs due to packets collision Fig. 12 The impact of varying node density on: a Packet loss probability, and b average energy consumed per data packet, and c network lifetime, and d end-to-end delay, using a maximum aggregation delay of 0.01 s that mostly depends on the number of transmissions and receptions within the network. In Fig. 12a, packet loss probability for ...
Context 9
... WSNs generally occurs due to packets collision Fig. 12 The impact of varying node density on: a Packet loss probability, and b average energy consumed per data packet, and c network lifetime, and d end-to-end delay, using a maximum aggregation delay of 0.01 s that mostly depends on the number of transmissions and receptions within the network. In Fig. 12a, packet loss probability for class-enCRT-NoAgg and alpha-enCRT-NoAgg mech- anisms increases, whereas, in the case of class-uniCRT-Agg, class-enCRT-Agg and alpha- enCRT-Agg, the curves increase gradually from 60-node network to 80-node network but tend to become almost constant later. This behavior can be explained by correlating this ...
Context 10
... mech- anisms increases, whereas, in the case of class-uniCRT-Agg, class-enCRT-Agg and alpha- enCRT-Agg, the curves increase gradually from 60-node network to 80-node network but tend to become almost constant later. This behavior can be explained by correlating this figure with the figure for normalized number of packet transmissions (see Fig. 11). Initially, normalized number of transmission increases thus we observe an increase in packet loss prob- ability. However, when there are large number of nodes, the normalized number of packet transmissions tends to increase gradually thus we observe that packet loss probability remains almost constant. It is observed that ...
Context 11
... average energy consumed per data packet received by sink versus number of nodes in shown in Fig. 12b. This can be considered as the inverse of energy efficiency. Hence, a lower value refers to a more efficient communication. The increase in the number of nodes leads to an increase in the average communication cost (in terms of number of total number of transmission and receptions) and packet loss probability (see Fig. 12a), and hence ...
Context 12
... of nodes in shown in Fig. 12b. This can be considered as the inverse of energy efficiency. Hence, a lower value refers to a more efficient communication. The increase in the number of nodes leads to an increase in the average communication cost (in terms of number of total number of transmission and receptions) and packet loss probability (see Fig. 12a), and hence an increase in the average energy consumed per data packet delivered to the sink, as depicted in Fig. 12b. For class-enCRT-NoAgg and alpha-enCRT-NoAgg, the increase is continuous and steady, whereas, for mechanisms employing aggregation the increase is gradual. This is because the increase in normalized number of ...
Context 13
... to a more efficient communication. The increase in the number of nodes leads to an increase in the average communication cost (in terms of number of total number of transmission and receptions) and packet loss probability (see Fig. 12a), and hence an increase in the average energy consumed per data packet delivered to the sink, as depicted in Fig. 12b. For class-enCRT-NoAgg and alpha-enCRT-NoAgg, the increase is continuous and steady, whereas, for mechanisms employing aggregation the increase is gradual. This is because the increase in normalized number of transmissions (see Fig. 11) is gradual, whereas, in the case of no aggregation the increase is much higher. We observe that ...
Context 14
... and hence an increase in the average energy consumed per data packet delivered to the sink, as depicted in Fig. 12b. For class-enCRT-NoAgg and alpha-enCRT-NoAgg, the increase is continuous and steady, whereas, for mechanisms employing aggregation the increase is gradual. This is because the increase in normalized number of transmissions (see Fig. 11) is gradual, whereas, in the case of no aggregation the increase is much higher. We observe that DAA+RW does not give better performance than the rest of aggregation mechanisms (class-uniCRT-Agg, class-enCRT-Agg and alpha-enCRT-Agg) in terms of energy efficiency. Figure 12c shows the network lifetime versus number of nodes. Network ...
Context 15
... observe that DAA+RW does not give better performance than the rest of aggregation mechanisms (class-uniCRT-Agg, class-enCRT-Agg and alpha-enCRT-Agg) in terms of energy efficiency. Figure 12c shows the network lifetime versus number of nodes. Network lifetime is defined as the time until the first node death, i.e., the time until the first node exhausts its energy. ...
Context 16
... better performance than the rest of aggregation mechanisms (class-uniCRT-Agg, class-enCRT-Agg and alpha-enCRT-Agg) in terms of energy efficiency. Figure 12c shows the network lifetime versus number of nodes. Network lifetime is defined as the time until the first node death, i.e., the time until the first node exhausts its energy. As shown in Fig. 11, the normalized number of transmissions for alpha-enCRT- Agg is less than class-enCRT-Agg and class-uniCRT-Agg. DAA+RW exhibits the lowest normalized number of transmissions (Fig. 11) but its packet loss probability is higher (Fig. 12a) and lesser energy efficient than the other aggregation mechanisms (Fig. 12b). Therefore, ...
Context 17
... versus number of nodes. Network lifetime is defined as the time until the first node death, i.e., the time until the first node exhausts its energy. As shown in Fig. 11, the normalized number of transmissions for alpha-enCRT- Agg is less than class-enCRT-Agg and class-uniCRT-Agg. DAA+RW exhibits the lowest normalized number of transmissions (Fig. 11) but its packet loss probability is higher (Fig. 12a) and lesser energy efficient than the other aggregation mechanisms (Fig. 12b). Therefore, alpha-enCRT-Agg is expected to have a higher network lifetime than the others, as depicted in Fig. 12c. The mechanisms employing aggregation have higher network lifetime as compared to the ...
Context 18
... as the time until the first node death, i.e., the time until the first node exhausts its energy. As shown in Fig. 11, the normalized number of transmissions for alpha-enCRT- Agg is less than class-enCRT-Agg and class-uniCRT-Agg. DAA+RW exhibits the lowest normalized number of transmissions (Fig. 11) but its packet loss probability is higher (Fig. 12a) and lesser energy efficient than the other aggregation mechanisms (Fig. 12b). Therefore, alpha-enCRT-Agg is expected to have a higher network lifetime than the others, as depicted in Fig. 12c. The mechanisms employing aggregation have higher network lifetime as compared to the mechanisms without aggregation since aggregation reduces ...
Context 19
... node exhausts its energy. As shown in Fig. 11, the normalized number of transmissions for alpha-enCRT- Agg is less than class-enCRT-Agg and class-uniCRT-Agg. DAA+RW exhibits the lowest normalized number of transmissions (Fig. 11) but its packet loss probability is higher (Fig. 12a) and lesser energy efficient than the other aggregation mechanisms (Fig. 12b). Therefore, alpha-enCRT-Agg is expected to have a higher network lifetime than the others, as depicted in Fig. 12c. The mechanisms employing aggregation have higher network lifetime as compared to the mechanisms without aggregation since aggregation reduces communi- cation cost. The class-enCRT-Agg improves the network lifetime by 16 % ...
Context 20
... class-enCRT-Agg and class-uniCRT-Agg. DAA+RW exhibits the lowest normalized number of transmissions (Fig. 11) but its packet loss probability is higher (Fig. 12a) and lesser energy efficient than the other aggregation mechanisms (Fig. 12b). Therefore, alpha-enCRT-Agg is expected to have a higher network lifetime than the others, as depicted in Fig. 12c. The mechanisms employing aggregation have higher network lifetime as compared to the mechanisms without aggregation since aggregation reduces communi- cation cost. The class-enCRT-Agg improves the network lifetime by 16 % as compared to class-enCRT-NoAgg, whereas, alpha-enCRT-Agg improves by 16-17 % when compared to alpha-enCRT-NoAgg. ...
Context 21
... each simulation iteration, the average ETE delays experienced by all data packets received at the sink is computed. Figure 12d shows the graph for average ETE delay versus number of nodes. In a data aggregation system, data packets at the aggregator nodes have to wait for packets from other nodes for aggregation. ...
Context 22
... Figure 12d shows the graph for average ETE delay versus number of nodes. In a data aggregation system, data packets at the aggregator nodes have to wait for packets from other nodes for aggregation. Therefore, the curves for mechanism with aggregation will have a higher ETE delay as compared to the one without aggregation. In can be observed in Fig. 12d that the curves for all the techniques increase as number of nodes in the network increases. The alpha-enCRT-Agg reduces the average ETE delay by 7-11 % when compared to class-uniCRT-Agg and 4-8 % when compared to class- enCRT-Agg. The class-enCRT-Agg increases average ETE delay by 22-24 % as compared to class-enCRT-NoAgg whereas ...
Context 23
... percentage reduction in the normalized number of packet transmissions and aver- age energy consumed per data packet are analyzed from Figs. 11 and 12b, respectively. Figure 13a gives the percentage reduction for normalized number of packet transmissions for different network sizes. The alpha-enCRT-Agg reduces the normalized number of trans- missions by 12-20 % when compared to class-uniCRT-Agg and 6-10 % when compared to class-enCRT-Agg. The class-enCRT-Agg reduces the normalized ...
Context 24
... percentage reduction in the normalized number of packet transmissions and aver- age energy consumed per data packet are analyzed from Figs. 11 and 12b, respectively. Figure 13a gives the percentage reduction for normalized number of packet transmissions for different network sizes. The alpha-enCRT-Agg reduces the normalized number of trans- missions by 12-20 % when compared to class-uniCRT-Agg and 6-10 % when compared to class-enCRT-Agg. ...
Context 25
... class-enCRT-Agg reduces the normalized number of transmission by 25-27 % as compared to class-enCRT-NoAgg whereas alpha-enCRT-Agg reduces by 26- 33 % when compared to alpha-enCRT-NoAgg. Figure 13b gives the percentage reduction in the average energy consumed per data packet for different network sizes. The alpha- enCRT-Agg reduces the average energy consumed per data packet by 17-22 % when com- pared to class-uniCRT-Agg and 5-10 % when compared to class-enCRT-Agg. ...
Context 26
... have shown these values in our analysis so as to make it easy for the reader to analyze the effect of the proposed mechanisms on the network. Figure 14a, b give the total number of RTS,CTS, DATA and ACK packets transmitted and received versus aggregation delay, respectively. Number of packet transmissions and receptions reduces with an increase in aggregation delay as more data packets are available for aggregation. ...
Context 27
... total number of RTS,CTS, DATA and ACK packets transmitted and received versus aggregation delay, respectively. Number of packet transmissions and receptions reduces with an increase in aggregation delay as more data packets are available for aggregation. Thus these graphs follow the same trend as normalized number of packet transmissions shown in Fig. 15a. A data packet is received by more than one node, thus number of packet receptions is higher than the number of packet transmissions. Therefore, we observe that the decrease in the number of packet receptions is slightly higher than the number of packet transmissions. Figure 15a gives the normalized number of packet transmissions ...
Context 28
... we observe that the decrease in the number of packet receptions is slightly higher than the number of packet transmissions. Figure 15a gives the normalized number of packet transmissions versus aggregation delay. The normalized number of packet transmissions for class-uniCRT-Agg, class-enCRT-Agg and alpha-enCRT-Agg decreases with an increase in aggregation delay. ...
Context 29
... delay, more data packets are available at a particular node and at the same time for aggregation, thus reducing the normalized number of packet transmissions accordingly. The normalized number of packet transmissions reduces rapidly until aggrega- tion delay equals 0.008 s. After this point, the decrease in the normalized number of packet Fig. 15 The impact of varying aggregation delay on: a Normalized number of packet transmissions, and b average energy consumed per data packet in an 80-node random network topology transmissions tends to become gradual. Data transfer between two nodes takes about 0.007 s and when the aggregation delay increases above this value, most of the ...
Context 30
... a comparatively gradual decrease in the normalized number of packet transmissions after 0.008 s. Energy consumed in the network is directly related to the total number of transmissions and receptions. Since the number of packets transmitted and received decreases, therefore, the average energy consumed should also decrease. This is depicted in Fig. 15b for the average energy consumed per data packet delivered to sink node. The curves in this figure follow a similar trend as that in Figs. 14a, b and 15a. This is because with an increase in an aggregation delay, more data packets are aggregated by each aggregator node. Figure 16a shows the network lifetime versus aggregation delay. We ...
Context 31
... related to the total number of transmissions and receptions. Since the number of packets transmitted and received decreases, therefore, the average energy consumed should also decrease. This is depicted in Fig. 15b for the average energy consumed per data packet delivered to sink node. The curves in this figure follow a similar trend as that in Figs. 14a, b and 15a. This is because with an increase in an aggregation delay, more data packets are aggregated by each aggregator node. Figure 16a shows the network lifetime versus aggregation delay. We can observe from Fig. 15b that with a decrease in average energy consumption, there is an increase in network lifetime in Fig. 16a. As we keep on ...
Context 32
... is because with an increase in an aggregation delay, more data packets are aggregated by each aggregator node. Figure 16a shows the network lifetime versus aggregation delay. We can observe from Fig. 15b that with a decrease in average energy consumption, there is an increase in network lifetime in Fig. 16a. ...
Context 33
... average energy consumed per data packet delivered to sink node. The curves in this figure follow a similar trend as that in Figs. 14a, b and 15a. This is because with an increase in an aggregation delay, more data packets are aggregated by each aggregator node. Figure 16a shows the network lifetime versus aggregation delay. We can observe from Fig. 15b that with a decrease in average energy consumption, there is an increase in network lifetime in Fig. 16a. As we keep on increasing the aggregation delay, we can observe that the slope for the curves of class-uniCRT-Agg, class-enCRT-Agg and alpha-enCRT-Agg decreases. This emphasizes that there is an upper bound on the network lifetime ...
Context 34
... trend as that in Figs. 14a, b and 15a. This is because with an increase in an aggregation delay, more data packets are aggregated by each aggregator node. Figure 16a shows the network lifetime versus aggregation delay. We can observe from Fig. 15b that with a decrease in average energy consumption, there is an increase in network lifetime in Fig. 16a. As we keep on increasing the aggregation delay, we can observe that the slope for the curves of class-uniCRT-Agg, class-enCRT-Agg and alpha-enCRT-Agg decreases. This emphasizes that there is an upper bound on the network lifetime in WSNs, as studied in [31]. The class-enCRT-Agg improves the network lifetime by 12-15 % as compared to ...
Context 35
... alpha-enCRT-Agg also exhibits superior performance than DAA+RW in terms of network lifetime. Figure 16b gives the average ETE delay versus aggregation delay. As expected, the curves for class-uniCRT-Agg, class-enCRT-Agg and alpha-enCRT-Agg increase with an increase in the aggregation delay. ...
Context 36
... class-enCRT-Agg increases the average ETE delay by 12- 36 % as compared to class-enCRT-NoAgg whereas alpha-enCRT-Agg increases by 15-36 % when compared to alpha-enCRT-NoAgg. Figures 15b and 16b reveal that there is a trade-off between energy saving and ETE delay in the aggregation process. Efficient aggregation in WSNs will result in high energy saving at the expense of increased ETE delay. ...
Context 37
... compute the percentage reduction in the normalized number of packet transmissions and average energy consumed per data packet from Fig. 15a, b, respectively. Figure 17a gives the percentage reduction in the normalized number of packet transmissions versus aggregation delay. The alpha-enCRT-Agg reduces the normalized number of packet trans- missions from 20 to 40 % when compared to alpha-enCRT-NoAgg whereas class-enCRT-Agg reduces the normalized number of packet ...
Context 38
... compute the percentage reduction in the normalized number of packet transmissions and average energy consumed per data packet from Fig. 15a, b, respectively. Figure 17a gives the percentage reduction in the normalized number of packet transmissions versus aggregation delay. The alpha-enCRT-Agg reduces the normalized number of packet trans- missions from 20 to 40 % when compared to alpha-enCRT-NoAgg whereas class-enCRT-Agg reduces the normalized number of packet transmission by 18-35 % when compared to class- enCRT-NoAgg. ...
Context 39
... number of packet transmission by 18-35 % when compared to class- enCRT-NoAgg. The alpha-enCRT-Agg reduces normalized number of packet transmissions by 12-26 % as compared to class-uniCRT-Agg and 7-13 % as compared to class-enCRT- Agg. As for the average energy consumed per data packet, the percentage reduction versus aggregation delay is shown in Fig. 17b. The alpha-enCRT-Agg reduces the average energy consumed per data packet by 14-24 % as compared to class-uniCRT-Agg and 5-9 % as compared to class-enCRT-Agg. The class-enCRT-Agg reduces the average energy consumed per data packet by 22-34 % as compared to class-enCRT-NoAgg whereas alpha-enCRT-Agg reduces by 19-35 % as compared to ...
Similar publications
Energy efficiency is an important demand of Wireless Sensor Networks (WSNs). Through data aggregation, energy efficiency will be improved by filtering wrong information and merge redundant ones. If invalid transmitting is cut down, the nodes’ energy will be consumed and the utilization of wireless channel will be increased. Designing data aggregati...
Citations
... The data processed by arithmetic mean and batch estimation [13] shows in Table 2. The local data obtained after abnormal data processing adopts adaptive weighted fusion algorithm [13][14][15][16][17], it obtains more effective fusion effect than average estimation. ...
... there are three levels of coverage: local, scalable, and global, existing INSs ranges are usually from 5 to 50 m, Coverage is closely related to accuracy Wi-Fi fingerprint localization[78], Horizontal Placement i Beacon[79] RP Clustering and Coarse Localization[80] [81][82]Learning the possibility of moving each node to reduce the number of individual nodes as much as possible based on virtual areas by selecting nodes based on learning among nodes.[83] Scalability of an INS means the system ensures the normal positioning function when it scales in one of two dimensions: geography and number of users, indicates that the number of units located per time period per geographic area increases Location-Aware Communications for 5G Networks[84], BLE profile adaptor uses DDS[85] Multi-Agent paradigm-the high number of sensors which requires flexible communication mechanisms to guarantee scalability[86], Infrastructure Perspective[87] Cost money, time, space, and energyThis technique can be implemented with WiFi, BLE and Zig bee, i Beacon standard introduced by Apple is based on RSSI[61] ...
Internet of Things (IoT) is turning into an essential part of daily life, and numerous IoT-based scenarios will be seen in future of modern cities ranging from small indoor situations to huge outdoor environments. In this era, navigation continues to be a crucial element in both outdoor and indoor environments, and many solutions have been provided in both cases. On the other side, recent smart objects have produced a substantial amount of various data which demands sophisticated data mining solutions to cope with them. This paper presents a detailed review of previous studies on using data mining techniques in indoor navigation systems for the loT scenarios. We aim to understand what type of navigation problems exist in different IoT scenarios with a focus on indoor environments and later on we investigate how data mining solutions can provide solutions on those challenges.
... The aggregator nodes continue to broadcast the "hello" message to the lower neighbor node to recruit the child nodes. When the recruiting behaviors are over in the network, the construction of the data aggregation tree is complete [28][29][30]. ...
The privacy-preserving of information is one of the most important problems to be solved in wireless sensor network (WSN). Privacy-preserving data aggregation is an effective way to protect security of data in WSNs. In order to deal with the problem of energy consumption of the SMART algorithm, we present a new dynamic slicing D-SMART algorithm which based on the importance degree of data. The proposed algorithm can decrease the communication overhead and energy consumption effectively while provide good performance in preserving privacy by the reasonable slicing based on the importance degree of the collected raw data. Simulation results show that the proposed D-SMART algorithm improve the aggregation accuracy, enhance the privacy-preserving, reduce the communication overhead to some extent, decrease the energy consumption of sensor node and prolong the network lifetime indirectly.
... Performance of proposed model has been evaluated in simulated environment which clearly shows that proposed model performs better in terms of power consumption and network lifetime. Awang and Agarwal [80] proposed data aggregation technique where aggregator nodes are determined speculatively without being dependent on global knowledge i.e. nodes' geographical location, network topology and data flow. Received signal strength indicator (RSSI) is used to aggregate and route data packets. ...
Wireless sensor networks (WSNs) consist of large number of small sized sensor nodes, whose main task is to sense the desired phenomena in a particular region of interest. These networks have large number of applications such as habitat monitoring, disaster management, security and military etc. Sensor nodes are very small in size and have limited processing capability as these nodes have very low battery power. WSNs are also prone to failure, due to low battery power constraint. Data aggregation is an energy efficient technique in WSNs. Due to high node density in sensor networks same data is sensed by many nodes, which results in redundancy. This redundancy can be eliminated by using data aggregation approach while routing packets from source nodes to base station. Researchers still face trouble to select an efficient and appropriate data aggregation technique from the existing literature of WSNs. This research work depicts a broad methodical literature analysis of data aggregation in the area of WSNs in specific. In this survey, standard methodical literature analysis technique is used based on a complete collection of 123 research papers out of large collection of 932 research papers published in 20 foremost workshops, symposiums, conferences and 17 prominent journals. The current status of data aggregation in WSNs is distributed into various categories. Methodical analysis of data aggregation in WSNs is presented which includes techniques, tools, methodology and challenges in data aggregation. The literature covered fifteen types of data aggregation techniques in WSNs. Detailed analysis of this research work will help researchers to find the important characteristics of data aggregation techniques and will also help to select the most suitable technique for data aggregation. Research issues and future research directions have also been suggested in this research literature.
... Performance of proposed model has been evaluated in simulated environment which clearly shows that proposed model performs better in terms of power consumption and network lifetime. Awang and Agarwal [80] proposed data aggregation technique where aggregator nodes are determined speculatively without being dependent on global knowledge i.e. nodes' geographical location, network topology and data flow. Received signal strength indicator (RSSI) is used to aggregate and route data packets. ...
A Wireless Sensor Network (WSN) has gained a tremendous attention of researchers with its dynamic applications. The constant monitoring of critical scenarios has make WSNs an attractive choice for researchers at a large scale. The main objective is to increase the network lifetime for optimal and efficient utilization of resources in WSNs. For optimum functionality, various approaches have been proposed based upon clustering. Network lifetime is related with energy level of sensor nodes deployed in region of interest. As sensor nodes have limited lifetime, so there is a need to develop an algorithm for aggregating the sensors data in WSNs. A novel Dynamic Adaptive Hierarchical Data Aggregation (DAHDA) algorithm has been presented for evolving, uniform and non-uniform networks while maintaining the data accuracy. In addition, the algorithm is able to handle sudden bursts in the underlying data by recording the data in the area of interest for the whole event duration. The experimental evaluation on real and synthetic data shows that the algorithm performs well in terms of extending the lifetime of the network, maintaining the original distribution of the sensors as long as possible and maintaining the accuracy of the sensed data. DAHDA is an adaptive hierarchical aggregation algorithm for WSNs. The proposed algorithm has been simulated and its performance has been compared with the existing approaches in terms of residual energy, number of alive nodes, data accuracy, sudden burst detection, sensor distribution, lifetime of last node, first node and average lifetime of node for uniform, non-uniform and evolving networks.
Information technology continues to promote the in-depth progress and development of smart agriculture, while the corresponding agricultural economic development is increasingly dependent on the degree and level of agricultural informatization. At present, the development of digital and information greenhouse technology has become the focus and difficulty of agricultural development. Based on this, this paper will design a set of agricultural digital greenhouse system based on ZigBee wireless sensor network technology. At the same time, in the corresponding data acquisition and processing problems, this paper uses the PID controller under the particle filter optimization technology to optimize the error of the corresponding data acquisition system, and eliminate the corresponding noise and interference, so as to ensure the stability and effectiveness of the corresponding data acquisition system in the digital greenhouse, reduce the power consumption of the whole system, and ensure the stable transmission of data. Experimental results show that the proposed digital greenhouse technology can not only improve crop yield rapidly, but also optimize the noise control level of key algorithm by 10 points compared with the traditional filtering technology, so it has a long-term promotion significance.
Aiming at three-dimensional space localization problem in wireless sensor network, a method of three-dimensional centroid algorithm with spherical perception radius (3DCSPR) is proposed. The algorithm uses Gaussian probability density function to estimate the sensor node radius, and uses tetrahedron method and three-dimensional center to locate unknown node with estimated. Firstly, RSSI value between beacon nodes and destination node is selected by RSSI distribution density function, that confidence interval of nodes perception radius is estimated. Secondly, satisfied tetrahedron is got by the intersections sphere coverage of beacon nodes in three-dimensional. Finally, destination node localization is calculated with weighted centroid in three-dimensional. Simulation results show that the proposed method can significantly reduce positioning errors in three-dimensional network environment in reality and can be widely applied to many fields.
